Methods for automatic segmentation and temporal tracking

ABSTRACT

In one embodiment, a method of detecting centerline of a vessel is provided. The method comprises steps of acquiring a 3D image volume, initializing a centerline, initializing a Kalman filter, predicting a next center point using the Kalman filter, checking validity of the prediction made using the Kalman filter, performing template matching, updating the Kalman filter based on the template matching and repeating the steps of predicting, checking, performing and updating for a predetermined number of times. Methods of automatic vessel segmentation and temporal tracking of the segmented vessel is further described with reference to the method of detecting centerline.

FIELD OF INVENTION

The invention relates in general to medical imaging, and moreparticularly to an improved segmentation and tracking method forreal-time 3-dimensional ultrasound (real time 3-D US).

BACKGROUND OF THE INVENTION

Several medical procedures require placement of catheters inside a bloodvessel of a human body. Placement of central catheters is currentlyperformed blindly and then confirmed with X-ray after completion of themedical procedure. X-ray imaging has adequate resolution to see tinyvessels but also would cause radiation-related complications.

Toward improved and safer care for patients including fragile infants,real-time 3-D ultrasound imaging have been proposed to supplement orgradually replace current X-ray imagers to help clinicians in performingcatheter insertion operation.

The real-time and radiation-free imaging capabilities of ultrasound makeit a more appealing option than X-ray-based imagers for guidinginterventional procedures. Further, usage of ultrasound images inreal-time, may facilitate carrying out the medical procedure in manyways, including improved outcomes for the infants and quicker completionof the medical procedures. However, ultrasound image suffers from heavyspeckle noise and lower spatial resolution. It is challenging for aclinician to visualize and follow the moving blood vessels in the raw,real-time images when both hands are occupied wherein one hand holds andsweeps the probe; and the other handles the catheter delicately.According to clinical literature, improper positioning of centralcatheters is a suspected cause of severe complications that may lead todeath of fragile patients.

On the other hand, when performing diagnostic procedures, medicalpersonnel that perform these procedures are not trained in the use ofultrasound and thus are not used to the images procured by medicalultrasound systems. Several methods have been proposed in the prior artdescribing ways in which ultrasound-imaging methods can be used toenhance visualization.

Some of the limitations associated with the prior art methods include,low image quality and signal to noise ratio, large motion of vessel ofinterest, small size of the vessel of interest, disappearance ofportions of the vessel due to artifacts etc, clutter in the data due topresence of other structures and/or vessels in the vicinity of thevessel of interest, high temporal frame rate of data and the need forsegmentation and tracking to match the data acquisition speed.

In particular, one of the prior art method describes an algorithm forsegmenting a vessel cross section in a single 2D slice, and thentracking the segmented vessel in a single 3D volume. This method doesnot address the temporal tracking aspect, i.e., the method does notdescribe updating the vessel segmentation over time to account formotion.

Several approaches for vessel segmentation or tubular structuresegmentation have been proposed for other imaging modalities. However,the methods are not applicable to ultrasound data and temporal trackingapplications.

Hence there exists a need for a method for tracking blood vessels thatis simple, provides time efficient tracking of blood vessels that iseasy to visualize and comprehend, robust in complex environments andcomputationally efficient

BRIEF DESCRIPTION OF THE INVENTION

The above-mentioned shortcomings, disadvantages and problems areaddressed herein which will be understood by reading and understandingthe following specification.

In one embodiment, a method of detecting centerline of a vessel isprovided. The method comprises steps of acquiring a 3D image volume,initializing a centerline, initializing a Kalman filter, predicting anext center point using the Kalman filter, checking validity of theprediction made, performing template matching based centerline detectionfor estimating measurement error, updating the Kalman filter based onthe template matching and repeating the steps of predicting, checking,performing and updating until the end of the vessel or for apredetermined number of times.

In another embodiment, an automated segmentation method for generating asurface contour of a vessel is provided. The method comprises steps ofinitializing a vessel cross section, initializing an iteration counter,evolving an active contour towards a vessel boundary, regularizing acontour from the active contour evolution by using least square fitting,determining if the value of the iteration counter is greater than apredetermined value, performing re initialization using the regularizedcontour if the value of the iteration counter is less than thepredetermined value, incrementing the iteration counter and repeatingthe steps of evolving, regularizing, determining, performing andincrementing until the entire vessel has been segmented.

In yet another embodiment, a method of segmenting a vessel comprising aplurality of cross sections is provided. The method comprises steps ofacquiring a 3D image volume, initializing one or more Kalman filterparameters using a preset configuration file, selecting an initialcenter point within the vessel to be segmented, performing an initialsegmentation on a first image slice based on the initial center point,the first image slice being a 2D cross-section of the vessel, creating atemplate of a vessel cross section based on the initial segmentation,predicting a next center point that is a translation of the initialcenter point along a beam direction using the Kalman filter, correctingthe next center point based on a measurement of the next center point inthe image volume, segmenting a second image slice based on the nextcenter point and repeating the steps of predicting, correcting andsegmenting until the vessel has been completely segmented in the 3Dimage volume.

In another embodiment, a method of vessel temporal tracking is provided.The method comprises steps of: acquiring a 3D image volume, initializingone or more Kalman filter parameters using a preset configuration file,identifying a first image slice, performing an initial segmentation onthe first image slice, selecting an initial center point of thesegmented image slice, creating a template of a vessel cross sectionaround the initial center point based on the initial segmentation, thetemplate being an elliptical model, finding a next center point by thesteps of: copying parameters of the adapted elliptical model to aplurality of candidate points neighboring the initial center point,orienting an elliptical adaptable model around each of the plurality ofcandidate points using the copied parameters, searching for centerpoints around each of the candidate points based on the ellipticaladapted model around each of the candidate points, adapting theelliptical adaptable models around the candidate points to the foundcenter points, selecting one of the candidate points whose adapted modelfits best to the vessel as the next center point and repeating the stepof finding a next center point until an end point of the vesselcenterline or a predetermined number of iterations are reached.

In yet another embodiment, a computer system comprising: a processor anda program storage device readable by the computer system, embodying aprogram of instructions executable by the processor to perform methodsteps for automatic segmentation and temporal tracking of a blood vesselin a 2D or 3D image data set by extracting a centerline along the bloodvessel in a selected region is provided. The method comprises steps ofacquiring an image volume from an ultrasound imaging system, performingvessel segmentation on the 3D image volume to generate a 3D ultrasoundimage of the blood vessel, detecting a vessel centerline for thesegmented blood vessel, estimating cross-sectional area of the segmentedblood vessel using the detected vessel centerline and performingtemporal tracking of the estimated cross-section based on templatematching.

Systems and methods of varying scope are described herein. In additionto the aspects and advantages described in this summary, further aspectsand advantages will become apparent by reference to the drawings andwith reference to the detailed description that follows.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 shows a flowchart that describes an over-view of atemplate-matching based vessel-centerline detection method described inone embodiment;

FIG. 2 shows a flow chart that describes the step of, initializing acenterline, shown in FIG. 1;

FIG. 3 shows a flow chart that describes the step of, acquiring vesseltemplate, shown in FIG. 2;

FIG. 4 shows a flow chart that describes the process of predicting anext center point, shown as a step in FIG. 1;

FIG. 5 shows a flow chart that describes performing template matchingdescribed as a step in FIG. 1;

FIG. 6 shows a flow chart that describes the step of updating the Kalmanfilter shown in FIG. 1;

FIG. 7A and FIG. 7B show a flow chart that describes an automatedsegmentation method for generating a surface contour described in oneembodiment;

FIG. 8 shows a flow chart that describes the initializing step shown inFIG. 7;

FIG. 9 illustrates a 3D imaging geometry and the coordinate systemscorresponding to sweeping a linear array in elevation direction;

FIG. 10 shows a flow chart that describes the step of identifyinginitial elliptical contour shown in FIG. 8;

FIG. 11A and FIG. 11B show a flow chart that describes a method ofsegmenting a vessel comprising plurality of cross sections described inanother embodiment;

FIG. 12 shows a schematic diagram depicting an exemplary method oftracking vessel cross-section in an imaging slice, as described in oneembodiment;

FIG. 13 shows a flow chart that describes a method of vessel temporaltracking as described in one embodiment; and

FIG. 14 shows a flow chart that describes the step of finding a nextcenter point shown in FIG. 13.

DETAILED DESCRIPTION OF THE INVENTION

In the following detailed description, reference is made to theaccompanying drawings that form a part hereof, and in which is shown byway of illustration specific embodiments, which may be practiced. Theseembodiments are described in sufficient detail to enable those skilledin the art to practice the embodiments, and it is to be understood thatother embodiments may be utilized and that logical, mechanical,electrical and other changes may be made without departing from thescope of the embodiments. The following detailed description is,therefore, not to be taken in a limiting sense.

Ultrasound is a widely used medical imaging modality. It is inexpensive,widely accessible, fast, and safe. Ultrasound image segmentation isrequired in a number of medical examinations. For example, inobstetrics, in measuring dimensions of various anatomical features ofthe fetus; in oncology, for outlining the prostate for radiationtreatment planning; in cardiovascular applications, for diagnosing deepvenous thrombosis (DVT) and atherosclerosis using segmented features inultrasound images.

Premium medical diagnostic ultrasound imaging systems require acomprehensive set of imaging modes. These are the major imaging modesused in clinical diagnosis and include timeline Doppler, color flowDoppler, B mode and M mode. In the B mode, such ultrasound imagingsystems create two-dimensional images of tissue in which the brightnessof a pixel is based on the intensity of the echo return. Alternatively,in a color flow imaging mode, the movement of fluid (e.g., blood) ortissue can be imaged. Measurement of blood flow in the heart and vesselsusing the Doppler effect is well known. The phase shift of backscatteredultrasound waves may be used to measure the velocity of the backscattersfrom tissue or blood. The Doppler shift may be displayed using differentcolors to represent speed and direction of flow. In the spectral Dopplerimaging mode, the power spectrum of these Doppler frequency shifts arecomputed for visual display as velocity-time waveforms.

In ultrasound imaging, it is useful to segment blood vessels forvisualization and for aiding and guiding procedures such as catheterplacement etc. Often volumetric data is collected over time, where itmay be important to maintain the correct segmentation by updating thesegmentation of the vessel of interest as it moves due to physiologicalor probe motion. The invention relates to ultrasound segmentationmethods, in a broad sense, focusing on techniques developed for medicalB-mode ultrasound images.

The vessel is selected from the group consisting of blood, lymph andlacteal. The vessel can be characterized based on the position of thevessel center, the local orientation of the vessel, a model of thevessel cross-section, and the intensity distribution of the vessel'svoxels.

As used herein, the term “image” refers to multi-dimensional datacomposed of discrete image elements (e.g., pixels for 2-D images andvoxels for 3-D images). The image may be, for example, a medical imageof a subject collected by computer tomography, magnetic resonanceimaging, ultrasound, or any other medical imaging system known to one ofskill in the art. The image may also be provided from non-medicalcontexts, such as, for example, remote sensing systems, electronmicroscopy, etc. The methods of the inventions are not limited to suchimages, and can be applied to images of any dimension, e.g. a 2-Dpicture or a 3-D volume.

In particular, the invention provides methods for performing real timesegmentation and temporal tracking of a vessel of interest fromvolumetric ultrasound image data. The segmentation consists of twoparts: (1) First detecting the centerline of the vessel; and (2)Estimating the boundary of the vessel (or a mask of the vessel) on a setof 2-D image slices that are uniformly spaced along the vessel alignmentdirection. The vessel alignment direction is assumed to be parallel tobeam direction. This segmentation method could be applied to eachtemporal frame to get a new segmentation of the vessel in presence ofmotion.

The cross-section estimation method described herein is based on activecontours (or snakes). Active contours can be divided into two classes:the edge based relying on the image gradient to attract the snakes; andthe region based that depend on region-based statistical features.Region based active contours excel over the edge based wherever clutterand noise precludes movement of the edge based contour. Furthermore,region based contours are much less sensitive to the initialization ofthe contour than the edge based. To ensure robustness, a region basedactive contour segmentation method is adopted which is driven bymean-difference binary flows. Active contours are computed using eitherlevel set or parametric method. The former allows possible topologicalchanges of curves, but is expensive in terms of computational cost. Forreal-time performance, the parametric method can be chosen to implementactive contours in the cross-section estimation method.

Alternatively, the invention proposes a temporal tracking method, inwhich one or more selected parameters of the vessel segmentation inchosen temporal frames can be updated. The segmentation result can thenbe used to improve the visualization of the vessel of interest and guidesubsequent imaging and procedures. This yields a real-timeimplementation of vessel segmentation and tracking.

Accordingly, in one embodiment, the invention describes a method usedfor the initial detection of a vessel-centerline in a first B-modeultrasound volume. The vessel centerline detection is important becauseit provides guidance for a physician for inserting a catheter throughthe vessel. Further, vessel centerline detection is vital in thevisualization of the vessel (for clinical use) as the subsequentvisualization steps (vessel cross-section estimation, temporal vesseltracking, catheter detection, etc) depend heavily on the accurate androbust detection of the vessel-centerline. In this method an assumptionis made that the vessel lies along one of the main co-ordinate axes(beam direction). Based on this assumption, the method performs 2Dtemplate based vessel centerline detection.

FIG. 1 shows a flowchart that describes an over-view of thetemplate-matching based vessel-centerline detection method 100. Themethod 100 comprises steps of acquiring a 3D image volume at step 102,initializing a centerline at step 104, initializing a Kalman filter atstep 106, predicting a next center point using the Kalman filter at step108, checking validity of the prediction made using the Kalman filter atstep 110, performing template matching at step 112, updating the Kalmanfilter based on the template matching at step 114 and repeating thesteps of predicting, checking, performing and updating for apredetermined number of times at step 116.

The step 104 of initializing a centerline is described in FIG. 2. Withreference to FIG. 2, step 104 comprises selecting an initial centerpoint from the image volume at step 202, acquiring a template of avessel cross section at step 204 and setting up centerline trackingparameters based on the initial center point and the acquired templateat step 206.

An initial center point is selected from the 3D image volume obtainedfrom the ultrasound imaging system. The initial center point thusidentified is also referred to as a seed point. Given an initial seedpoint, a Kalman filter that uses 2D template matching is employed.Kalman filters are used for tracking the seed point in real-timeultrasound images. The movement of the seed point through space isdescribed by a dynamic model and measurements are used to estimate theposition of the seed point.

The step 204 of acquiring template of vessel cross-section is furtherexplained in conjunction with FIG. 3. With reference to FIG. 3, step 204comprises steps of obtaining a mask from thresholding and morphologicalprocessing of image data step 302, estimating approximate vesseldimensions based on the mask obtained step 304, specifying a gate sizebased on the vessel dimensions; step 306 and creating a 2D template ofthe vessel cross-section centered at the initial center point and havingthe specified gate size step 308.

The 2D image-patch around the initial-seed point is used forinitializing the template that is used for detecting the vesselcross-section in subsequent slices. Further, the mask from thresholdingand morphological processing of the image data above is used to estimatethe approximate vessel dimensions.

These vessel dimensions help in specifying the template size (this isalso referred to as “gate-size”). Subsequent to the detection of theinitial seed point and the template initialization, a 2D template of thevessel cross-section centered at the initial seed point is created.

Referring back to FIG. 2, initializing a centerline at step 104 furthercomprises step 206 of setting up centerline tracking parameters. Theparameters for the centerline tracking are of two types: Kalman filterparameters and template matching parameters. Kalman filter parametersinclude process noise variance (Q), measurement error variance (R),prediction error variance (also referred to as estimation errorvariance) (P), Kalman Gain (K), Delta_T (time-step) and Lambda(parameter that is used to update the measurement error variance).

Template matching parameters include initial observation window size,motion-direction (desired motion along a selected dimension in the 3Dvolume), match-score threshold and alpha, parameter that controlsmatch-score computation (holds the ability to bias the template matchingresults in favor of the candidate that is closer to the prediction)

The Kalman filter estimates a process by using a form of feedbackcontrol: the filter estimates the process state at some time and thenobtains feedback in the form of (noisy) measurements. As such, theequations for the Kalman filter fall into two groups: time updateequations and measurement update equations. The time update equationsare responsible for projecting forward (in time) the current state anderror variance estimates to obtain the priori estimates for the nexttime step. The measurement update equations are responsible for thefeedback—i.e. for incorporating a new measurement into the a prioriestimate to obtain an improved posteriori estimate. The time updateequations can also be thought of as predictor equations, while themeasurement update equations can be thought of as corrector equations.The final estimation method resembles that of a predictor-correctormethod for solving numerical problems.

Referring back to FIG. 1, the method 100 at step 106 comprisesinitializing the Kalman filter. For initializing the Kalman filter theKalman filter parameters listed above are obtained from a presetconfiguration file. In subsequent time-steps, they get updated and areused for the subsequent time-steps.

The method 100 further comprises predicting a next center point usingthe Kalman filter at step 108. Step 108 is further explained inconjunction with FIG. 4. Using the Kalman filter to predict a nextcenter point comprises defining a transition matrix for translating theinitial center point by a spatial displacement along the beam direction,defining a prediction error variance P for the initial center point,predicting a next center point and a next prediction error variancebased on the transition matrix, defining process noise variance for theprediction error variance and the series of center point measurements,correcting the center point prediction based on a measurement errorvariance, a measurement of the predicted center point, and the processnoise variance of the center point measurements, and correcting theprediction error variance based on the measurement error variance andthe process noise variance of the center point measurements.

Accordingly, with reference to FIG. 4, step 108 comprises providing afirst estimated center point and velocity at step 402, providing valuesfor Kalman filter parameters at step 404, performing Kalman predictionat step 406, obtaining a predicted center point and velocity at step 408and updating prediction error variance at step 410.

In the Kalman filter, the time update projects the current stateestimate ahead in time. The measurement update adjusts the projectedestimate by an actual measurement at that time. The time updateequations project the state and variance estimates forward from timestep k−1 to step k. Accordingly, first estimated center and velocitycould also be equated to be last estimated center and velocity,represented by C_(k-1) and V_(k-1) respectively. However, for a firstiteration, the first estimated center point is the initial center pointor the seed point and the velocity is approximated to be a constant. Theoutputs obtained from the Kalman filter prediction are predicted center(C_(kp)) and velocity (V_(kp)) and updated prediction/estimation errorvariance P_(k).

The method 100 further comprises step 110 for checking validity of theprediction made using the Kalman filter at step 108. This step 110checks if the predicted Center (C_(kp)) is within the volume bounds, andalso that the estimation error is reasonable. Accordingly, step 110comprises checking if the predicted center point lies within apredetermined volume such as the acquired template and checking if theprediction error variance is below an estimation threshold. Theestimation threshold can be obtained from earlier detections.

Subsequent to checking the validity of prediction made using the Kalmanfilter at step 110, the method 100 further comprises performing templatematching at step 112. The template matching is performed between thenext center point estimated and a measured center point measured usingthe Kalman filter. When tracking features in ultrasound images overseveral frames, template matching is a common procedure; the feature tobe detected is described by a mask and a correlation procedure isperformed to determine its location.

Step 112 of performing template matching is further explained inconjunction with FIG. 5. With reference to FIG. 5, step 112 comprisessteps of providing the predicted center point and velocity as inputs atstep 502, providing updated prediction error variance at step 504,performing template matching at step 506, obtaining a measured centerpoint and velocity at step 508 and obtaining measurement error varianceat step 510. In one embodiment, the template matching is performed basedon Rayleigh approximation of speckle noise distribution. Rayleighapproximation is well known to those skilled in the art and hence willnot be described in detail.

The inputs provided to the Kalman filter for performing templatematching are predicted center (C_(kp)), velocity (V_(kp)) andprediction/estimation error variance P_(k) obtained from the predictionstep 108. A feedback is provided using measurement update equations forincorporating a new measurement into the a priori estimate to obtain animproved posteriori estimate. Accordingly, the outputs obtainedsubsequent to performing template matching at step 112 are measuredcenter (C_(km)) and velocity (V_(km)) and measurement error variance(R_(k)).

A measurement update at step 114 is carried out following the step 112as depicted in FIG. 1. Step 114 is further explained in conjunction withFIG. 6. With reference to FIG. 6, step 114 comprises steps of providingthe measured center point and velocity and measurement error variance(R_(k)) as inputs at step 602, obtaining a second estimated center pointand velocity at step 604 and updating the variable Kalman Gain (K) atstep 606.

In the Kalman filter update step 114, measured center (C_(km)) andvelocity (V_(km)) and measurement error variance (R_(k)) obtained asoutputs at step 112 are fed as inputs. A second estimated center (C_(k))and velocity (V_(k)) are obtained as outputs. As can be understood bythose skilled in the art, the second estimated center (C_(k)) is alsothe next center point.

The first task during the measurement update is to compute the Kalmangain. The next step is to actually measure the process, and then togenerate a posteriori estimate by incorporating the measurement. Thefinal step is to obtain a posteriori error variance estimate. The Kalmangain (K) thus obtained is updated thereafter for use in the futureiterations.

After each time and measurement update pair, the process is repeatedwith the previous posteriori estimates used to project or predict thenew priori estimates. Accordingly, the method 100 comprises repeatingthe steps of predicting, checking, performing and updating for apredetermined number of times at step 116. The predetermined number canbe selected based on the iterations required for reaching the end of thevessel. This recursive nature is one of the very appealing features ofthe Kalman filter as it makes practical implementations much morefeasible.

In another embodiment, as shown in FIG. 7A and FIG. 7B, an automatedsegmentation method 700 for generating a surface contour of the vesselis provided. The vessel segmentation uses an extended Kalman filter andan elliptical vessel model to determine the vessel boundary and estimateellipse parameters for that vessel, which can be used to quicklycalculate the transverse area.

The method 700 comprises steps of acquiring a 3-D image volume at step702, initializing a vessel cross section at step 704, initializing aniteration counter at step 706, evolving an active contour towards avessel boundary at step 708, regularizing a contour from the activecontour evolution by using least square fitting at step 710, determiningif the value of the iteration counter is greater than a predeterminedvalue at step 712, performing re initialization using the regularizedcontour if the value of the iteration counter is less than thepredetermined value at step 714, incrementing the iteration counter atstep 716 and repeating the steps of evolving, regularizing, determining,performing and incrementing for a predetermined number of iterationsindicated by the predetermined value at step 718.

The vessel cross-section segmentation method 700 is developed toautomatically delineate the boundaries of the vessels of interest from3D imagery and the estimated vessel centerlines. The boundary of avessel is drawn as a sequence of closed planar curves in a set ofparallel image planes uniformly distributed along the alignmentdirection of the vessel.

Step 704 of initializing is further explained in conjunction with FIG.8. With reference to FIG. 8, step 704 comprises detecting centerline ofa vessel using the vessel centerline detection method 100 at step 802,obtaining an initial center point estimated from the vessel centerlinedetection method 100 at step 804, identifying an initial ellipticalcontour at step 806 and setting up active contour parameters based onthe initial center point and the initial elliptical contour at step 808.

FIG. 9 illustrates a 3D imaging geometry and the coordinate systemscorresponding to sweeping a linear array in elevation direction. Thevessel of interest is aligned along the x-axis. It is convenientlyassumed that in any 2-D image slice, parallel to the (z, y) plane, thevessel cross-section appears in general as a convex region with anapproximately elliptical shaped boundary. The boundary can berepresented by three geometric parameters: the lengths of the major andminor axes and the orientation of the major axis measured from areference axis such as the z-axis (or column axis).

Step 806 of identifying initial elliptical contour is further explainedin conjunction with FIG. 10. With reference to FIG. 10, step 806comprises steps of determining lengths of the major (z-axis) and theminor axis (y-axis) based on the gate dimensions used in the vesselcenterline detection method 100, at step 1002 and setting theorientation of the major axis from a reference axis (z-axis in thisexemplary embodiment) approximately as equal to zero at step 1004.

Subsequent to identifying the initial elliptical contour at step 806,the method 704 of initializing the vessel cross-section furthercomprises setting up active contour parameters shown as step 808. Theparameters for the segmentation and tracking are selected automaticallybased on expected vessel size and depth. Step 808 of setting up activecontour parameters comprises specifying one or more active contourparameters. The one or more active contour parameters comprise weightparameters for area weighted mean difference, weight parameter for curvelength, time step size for curve evolution, maximum re-iteration valueand tolerance of contour convergence.

Active contour or snake approach is utilized to extract the curveoutlining the cross-sectional boundary of a vessel in any image plane.As the cross-sections of vessels resemble ellipses in general, thepermissible boundary curves are restricted to elliptical shape by leastsquared error fitting. Combining the detected centerline and estimatedcross-sections makes a complete and compact vessel model that not onlyeases a clinician's difficulty to correctly and precisely place andadvance the catheter, but also allows forming clinically significantviews of the catheter going through real-time 3D data.

The cross-section estimation procedure is performed by consecutivelytraversing the contour several times, typically three. The original seedpoint is used throughout, and a new seed point is not calculated untilthe next image is processed. The extracted contour and the reconstructedellipse are compared to each other, at additional computational expense,to insure smoothness for the contour. The root mean squared (rms) radialdistance between boundaries is used as an error measure, and is computedby measuring the distance between the estimated points and thecorresponding points on the generated ellipse. A data fit is deemedinvalid if this error is larger than a threshold.

An iteration counter is employed to keep track of the iterationsinvolved in evolving an active contour towards the vessel boundary. Ascan be seen from FIG. 7, the iteration counter is initialized at step706. Accordingly, the method 700 further comprises the step of evolvingthe initial elliptical curve toward the vessel boundary at step 708.

In one embodiment, given an initial sample, a number of likely modes arefirst determined or refined from an image using a conventional “activecontour technique” by performing an iterative search in a 2D imageplane. Conventional active contour techniques provide a deformablecurve, or “snake”, which moves over an image while minimizing itspotential energy. The energy of a snake can in general be divided intoan “internal energy” term constraining the overall shape of the snake,and an “external energy” function provided by the image driving thesnake towards a desired boundary. With an appropriate image energyfunction and careful initialization, an active contour snake canconverge effectively to the required boundary, thereby generating one ormore modes. Such conventional active contour techniques are well knownto those skilled in the art, and will not be discussed in further detail

Inputs for the step 708 for evolving the initial elliptical curvetowards the vessel boundary comprise the initial center point detectedby the vessel centerline detection method 100, centerline of the vesselstructure detected from the method 100, initial elliptical contourobtained from step 806 and active contour parameters. Output of thevessel cross-section estimation provides an N×5 array with each rowrepresenting the vessel center position (row and column indices) andcross section ellipse parameters (a, b, θ). The unit of θ is radian. Nis the number of centerline points. The slice index is not stored in thecross-section segmentation list as it is already contained in thecenterline list. It can be easily derived by using the slice-stepparameter.

The method 700 further comprises regularizing a contour from the activecontour evolution by using least square fitting of an ellipse to thecontour points at step 710. Inputs for ellipse fitting performed at step710 include a set of 2D points' coordinates (rows, cols). The returnedvector contains the center, radii, and orientation of the ellipse,stored as (Cx, Cy, Rx, Ry, theta). Theta is in radian unit. Thereby aninitial segmentation of the 3D image volume is performed. The resultingcurve may not necessarily be an ellipse. However, estimated ellipseparameters and the final search area from one image are used toinitialize the contour detection in the following image frame.

In yet another embodiment shown in FIG. 11A and FIG. 11B, a method 1100for segmenting a vessel comprising plurality of cross sections isprovided. The method 1100 comprises steps of acquiring a 3D image volumeat step 1102, initializing one or more Kalman filter parameters using apreset configuration file at step 1104, selecting an initial centerpoint within the vessel to be segmented at step 1106, performing aninitial segmentation on a first image slice based on the initial centerpoint, the first image slice being a 2D cross-section of the vessel, atstep 1108, creating a template of a vessel cross section based on theinitial segmentation at step 1110, predicting a next center point thatis a translation of the initial point along a beam direction, using theKalman filter, at step 1112, correcting the next center point based on ameasurement of the next center point in the image volume at step 1114,segmenting a second image slice of the vessel, based on the next centerpoint at step 1116 and repeating the steps of predicting, correcting andsegmenting until the vessel has been completely segmented in the 3Dimage volume, at step 1118.

Steps 1102 to 1110 can be understood from the description providedabove. Further, using the Kalman filter to predict a next center point,step 1112, comprises defining a transition matrix for translating theinitial center point by a spatial displacement along the beam direction,defining a prediction error variance P for the initial center point,predicting a next center point and a next prediction error variancebased on the transition matrix, defining process noise variance for theprediction error variance and the series of center point measurements,correcting the center point prediction based on a measurement errorvariance, a measurement of the predicted center point, and the processnoise variance of the center point measurements, and correcting theprediction error variance based on the measurement error variance andthe process noise variance of the center point measurements step 1114.

Subsequent to the step 1114, a second image slice of the vessel isobtained and segmented based on the corrected next center point at step1116. The method 1100 further comprises repeating the steps ofpredicting, correcting and segmenting until the vessel has beencompletely segmented in the 3D image volume, at step 1118.

Thus, the initial segmentation of the first ultrasound volume providesus with an extracted vessel centerline (CL₀), the centerline has to bemaintained (i.e., tracked) over time (to get CL₀, CL₁, . . . , CL_(t)etc). This is a challenging task because of numerous factors like, thetime delay between two consecutive acquisitions (δT_(aq)), nominal probemotion, motion of the blood vessel under consideration due to cardiacpumping, breathing, etc. accordingly, the invention further describes amethod of temporal centerline tracking.

Temporal tracking is an important part of vessel visualization, whichenables maintaining and visualizing the centerline of a blood vesselover time. Temporal Kalman filter is used to track and maintain thevessel centerline in real-time over successively acquired 3D volumes. Inpractice, the blood vessels are segmented in cross-sectional ultrasoundimages in real-time, with frame rates of 10-16 Hz or more, and thefeature location is tracked over successive 3D volumes using a temporalKalman filter

In order to perform this tracking, the initial segmentation of a vesselcross-section location is utilized to create a template of thecross-section and a Kalman filter based on template matching is used totrack this cross-section through consecutive temporal volumes. Theprocedure of tracking images involves finding the relative movementbetween adjacent image frames in the sequence of intravascularultrasound (IVUS) images, by calculating motion vectors, each of themotion vectors being the offset between adjacent image frames over asparse grid of image rectangles.

Typically, there exists no large deviation in the vessel center locationfrom one temporal slice to another and therefore the tracking parametersare initialized accordingly. For the Kalman filter dynamics, it isassumed that the vessel center moves with a constant velocity from frameto frame. The Kalman filter has two distinct phases: predict and update.The predict phase uses the estimate from the previous time step toproduce an estimate of the current state: In the update phase,measurement information from the current time step is used to refinethis prediction to arrive at a new, more accurate estimate.

The method used for tracking the vessel centerline from one temporalvolume to next is quite similar to the initial vessel centerlinedetection step in that a Kalman filter based tracking is performed andtemplate matching is used for Kalman filter measurement step.

Though this approach is similar to the initial segmentation, animportant difference is the template-matching measure used forcross-section tracking. A robust maximum-likelihood match measure isutilized that is derived by using the fact that the speckle in thecross-section image is approximated as a Rayleigh distribution.

FIG. 12 shows a schematic diagram depicting an exemplary method oftracking vessel-cross-section in an imaging slice-i. The temporaltracking for the “same” cross-sectional slice, i.e., S_(i) ^(t) is atemporally shifted image (at time t) of the “same” vessel cross-sectionslice S_(i) ^(t-1) in the previous time-slice (at time t−1). This allowsthe use of a similarity measure referred to herein as ‘CD₂ similaritymeasure’.

Using this similarity measure for template matching constraints thetemplate update step (as a weighted template update cannot be performed,as the weighted template would not adhere to the Rayleighapproximation). Thus, the “newly-tracked” vessel cross-section is usedas the updated template. This is a special case of the weighted updatewhere the weight given to the previous template is zero. This isefficient, as the CD₂ similarity measure has been shown to be robust fortemporal matching.

In the process of tracking the vessel cross-section each cross-sectionalslices are treated independent of each other (instead of a jointtracking method). To track the vessel as seen in say slice i (S_(i)^(t)), the center and vessel template from the slice i (S_(i) ^(t-1)) inthe previous temporal volume may be used. This initialization along withtracking parameters allows tracking the center point from S_(i) ^(t-1)to S_(i) ^(t). The steps shown in FIG. 1 are then carried out for eachsuch slice-i, where i=0, 1, . . . n.

Referring to the steps of selecting initial center and templatedescribed in the initialization step 104 of FIG. 1, for the first timeslice after the initial segmentation (t1), the initial center andvessel-template is provided by the segmentation result of (t0) given bythe vessel center point estimated in the vessel segmentation step. Forthe succeeding time slice (t2), the initialization (center, template andtracking parameters) may be obtained from the tracking result for t1,and so on.

With continued reference to FIG. 1, referring to the step of setting upcenterline tracking parameters described in the initialization step 104of FIG. 1, the centerline tracking parameters can be described ascomprising Kalman filter parameters and template matching parameters.

The Kalman filter parameters include process noise variance (Q) (set tobe a constant, the sensitivity of tracking results to this parameter islow), measurement noise variance (R) (noise variance obtained from theimage-based measurements i.e., the template matching score), estimateerror variance (P) (computed by the Kalman Filter based on themeasurement error and the system transition matrix (A), Kalman Gain (K)(updated at each step of the tracking), Delta_T (time-step, representsthe number of time slices that are skipped, for tracking from one timepoint to next, this is set to be 1) and Lambda (parameter that is usedto update the measurement error variance).

Template matching parameters include initial observation window size(related to the actual vessel dimensions, and can be pre-computed usinga default vessel size along with the acquisition geometry), match-scorethreshold (the template matching threshold used for determining if themeasurement values are “coasting” and Alpha (parameter that controlstotal-match-score computation and facilitates biasing the templatematching results in favor of the candidate that is closer to theprediction)

With reference to the Kalman filter initialization step 106 shown inFIG. 1, the Kalman filter parameters are initialized using a presetconfiguration file. In subsequent time-steps, they get updated and areused for the subsequent time-steps. For example, for tracking the centerpoint in slice-i of U, initial parameters from the configuration/testfile can be used. However, at the end of this tracking step, the Kalmanfilter parameters are updated based on the measurement. These updatedparameters are subsequently used for tracking the center point inslice-i of time-step t2, and so on.

With reference to the prediction step 108 shown in FIG. 1, theprediction step provides a predicted vessel center point based on thepast observations. The Kalman filter utilizes the measurement errorvariance computed during the “Update” stage of the previous trackingstep, in order to estimate the prediction (i.e., the contribution of theprevious center point is more if the measurement error during theestimation of the previous center point was small).

With continued reference to FIG. 1, the step 110 of checking validity ofKalman prediction includes, checking if the predicted center point(C_(kp)) is within the volume bounds, and also that the estimation erroris reasonable.

With reference to the Kalman filter based template-matching step 112shown in FIG. 1, the similarity between the estimated template and themeasured template is computed using the CD₂ similarity measure. Thissimilarity considers a maximum likelihood computation that utilizes theapproximate Rayleigh distribution of the speckle noise.

The CD₂ measure is especially useful when two temporally separatedslices/volumes of ultrasound data are being compared, which(approximately) correspond to the same physical location in the subject.If A=[A_(ij)] are the pixels of the vessel cross-section in S_(i) ^(t-1)and B=[B_(ij)] are the pixels of the same vessel cross-section in S_(i)^(t), then the multiplicative Rayleigh-noise approximation is used tocompute the match-score for A and B using maximum likelihood andtreating individual pixels independent of each other.

With reference to the subsequent Kalman filter updating step 114 shownin FIG. 1, the Measured Center point (C_(km)) and Velocity (V_(km))along with Measurement error variance (R_(k)) obtained from using CD2similarity measure are fed as inputs to obtain the next vessel centerpoint.

The first task during the measurement update is to compute the Kalmangain. The next step is to actually measure the process, and then togenerate a posteriori estimate by incorporating the measurement. Thefinal step is to obtain a posteriori error variance estimate. The Kalmangain (K) thus obtained is updated thereafter for use in the futureiterations.

The steps of predicting the vessel center point step 108, checkingvalidity of prediction step 110, performing template matching toestimate measurement error step 112 and subsequent updating of theKalman filter step 114 are repeated for each slice-i, (shown in FIG.12), where i=0, 1, . . . n.

FIG. 13 shows a flowchart describing an overview of the temporalcenterline-tracking method 1300 implemented for each image slice alongthe vessel, as described in another embodiment. The method of vesseltemporal tracking 1300 comprises steps of acquiring a 3D image volume atstep 1302, initializing the Kalman filter parameters using the presetconfiguration file at step 1304, identifying a first image slice at step1306, performing an initial segmentation on the first image slice atstep 1308, selecting an initial center point of the segmented imageslice at step 1310, creating a template of the vessel cross sectionaround the initial center point based on the initial segmentation, atstep 1312, finding a next center point at step 1314 and repeating thestep of finding the next center point until an end point of the vesselcenterline is reached or for a predetermined number of iterations, atstep 1316.

As described in one embodiment listed above, the template of the vesselcross-section is approximated to be an ellipse. Accordingly, an adaptiveelliptical model is employed in finding the next center point. Step 1314of finding the next center point is further explained in conjunctionwith FIG. 14. With reference to FIG. 14, step 1314 comprises steps ofcopying parameters of the adapted elliptical model to a plurality ofcandidate points neighboring the initial center point at step 1402,orienting an elliptical adaptable model around each of the plurality ofcandidate points using the copied parameters at step 1404, searching forcenter points around each of the candidate points based on theelliptical adapted model around each of the candidate points at step1406, adapting the elliptical adaptable models around the candidatepoints to the found center points at step 1408 and selecting one of thecandidate points whose adapted model fits best to the vessel as the nextcenter point at step 1410.

Step 1406 of searching for center points around each of the candidatepoints comprises performing morphological filtering to eliminatestructures smaller than a speckle size. Further, performingmorphological filtering includes steps of counting the number ofcontinuous vessels in the search region, computing the area of eachcontinuous vessels and rejecting vessels having an area lying outside apredetermined range.

In general, a target search region may contain more than one vesselsegment. The “best” vessel segment can be chosen based on any reasonablemorphological characteristic, such as the vessel diameter (closest to astandard size of the vessel type associated with the user-selectedapplication type), greatest vessel length or area, or most uniformdiameter (since the user usually moves the probe to obtain the best viewof the vessel of interest), or a combination of goodness measures.Alternatively, the best vessel segment can be defined as the one, whichis at the shortest distance from the preset position.

It is to be understood that embodiments of the present invention can beimplemented in various forms of hardware, software, firmware, specialpurpose processes, or a combination thereof. In one embodiment, thepresent invention can be implemented in software as an applicationprogram tangible embodied on a computer readable program storage device.The application program can be uploaded to, and executed by, a machinecomprising any suitable architecture.

Accordingly, in yet another embodiment, a computer system comprising: aprocessor and a program storage device readable by the computer system,embodying a program of instructions executable by the processor toperform method steps for automatic segmentation and temporal tracking ofa blood vessel is provided. The method comprises steps of acquiring animage volume from an ultrasound imaging system, performing vesselsegmentation on the 3D image volume to generate a 3D ultrasound image ofthe blood vessel, detecting a vessel centerline for the segmented bloodvessel, estimating cross-sectional area of the segmented blood vesselusing the calculated vessel centerline and performing temporal trackingof the estimated cross-section based on a template matching criteria.Each of the steps is described in methods illustrated in the aboveembodiments. The step of detecting a vessel centerline is described inmethod 100. The step of estimating the cross-sectional area of thesegmented vessel is described in method 700 and the step of performingtemporal tracking is described in method 1300.

A technical effect of one or more embodiments of the invention describedherein includes automated segmentation and tracking of three-dimensionalanatomical data formed from combinations of datasets, using a singlesoftware tool, for purposes of efficient diagnosis.

Another technical effect of the methods disclosed herein includesproviding information and images for planning and conductinginterventional procedures, thereby enabling a electrophysiologist,cardiologist, and/or surgeon to efficiently conduct the interventionalprocedure.

The feasibility of the methods described herein namely, the vesselcenterline detection, segmentation and temporal tracking has beendemonstrated using a synthetic (simulated static vessel phantom) and anin-vivo 3D datasets both of which do not contain any catheters.

The methods for vessel segmentation and tracking described hereinprovide an improved visualization capability to view all the vascularelements inside an acquisition volume procured using ultrasound imaging.The methods described herein aid the clinician by highlighting avascular tree of interest that helps the clinician distinguish thevascular tree from other surrounding anatomy. Further, the methods allowthe visualization of desired vessels even during the presence of motion.

Also, the methods described herein provide an improved visualization ofthe catheter. This helps a clinician in guiding a needle or catheter toa desired location while avoiding potential damage to the surroundingtissue. The segmented vessels not only ease the clinician's difficultyto correctly and precisely place and advance the catheter under theguidance of ultrasound, but also pave the way toward building moreclinically meaningful views of the catheter in the real-time 3D data,such as the vessel center-line curved cut views.

When the methods of vessel segmentation and tracking described hereinare used in diagnostic ultrasound imaging, the capability of the methodsto provide automatic segmentation and improved visualization results indecreasing the amount of training required to use the ultrasound imagingsystem.

Further, the methods described herein are automatic, that may be adaptedto select the best viewing planes (or volumes) for display, eliminatingthe need for complicated user interface.

The invention describes methods using a Kalman filter for centerlinedetection, segmentation and temporal tracking. Because the Kalman filteris designed to operate in the presence of noise, an approximate fit isoften good enough for the filter to be very useful. Thus, though, ingeneral, ultrasound images suffer from heavy speckle noise, the methodsdescribed herein can be more efficient in providing improved vesselvisualization.

in various embodiments of the invention, segmentation and trackingmethods for an ultrasound imaging system and an ultrasound imagingsystem using the segmentation and tracking methods are described.However, the embodiments are not limited and may be implemented inconnection with different applications. The application of the inventioncan be extended to other areas, for example other imaging devices.Specific clinical application areas include echocardiography, breastultrasound, transrectal ultrasound (TRUS), and intravascular ultrasound(IVUS)] and multiple methods that use assumed feature geometry. Theinvention provides a broad concept of using segmentation and trackingmethods to improve visualization, which can be adapted in any imagingsystem used for non-medical applications. The design can be carriedfurther and implemented in various forms and specifications.

This written description uses examples to describe the subject matterherein, including the best mode, and also to enable any person skilledin the art to make and use the subject matter. The patentable scope ofthe subject matter is defined by the claims, and may include otherexamples that occur to those skilled in the art. Such other examples areintended to be within the scope of the claims if they have structuralelements that do not differ from the literal language of the claims, orif they include equivalent structural elements with insubstantialdifferences from the literal language of the claims.

1. A method of detecting centerline of a vessel, the method comprising:acquiring a 3D image volume; initializing a centerline; initializing aKalman filter; predicting a next center point using the Kalman filter;checking validity of the prediction; performing template matching forestimating measurement error; updating the Kalman filter based on thetemplate matching; and repeating the steps of predicting, checking,performing and updating for a predetermined number of times.
 2. Themethod of claim 1, wherein the step of initializing comprises: selectingan initial center point from the image volume; acquiring a template of avessel cross section from the 3D image volume; and setting up centerlinetracking parameters based on the initial center point and the acquiredtemplate.
 3. The method of claim 2, wherein the step of acquiringtemplate comprises: obtaining a mask from thresholding and morphologicalprocessing of image data; estimating approximate vessel dimensions basedon the mask obtained; specifying a gate size based on the vesseldimensions; and creating a 2D template of the vessel cross-sectioncentered at the initial center point and having the specified gate size.4. The method of claim 3, wherein the vessel dimension represents aninternal diameter of a vessel segment.
 5. The method of claim 2, whereinsetting up centerline tracking parameters comprises: obtaining one ormore Kalman filter parameters, the Kalman filter parameters comprisingprocess noise variance (Q), measurement error variance (R), predictionerror variance (P) and Kalman gain (K); and obtaining one or moretemplate matching parameters, the template matching parameterscomprising initial observation window size, motion vector andmatch-score threshold.
 6. The method of claim 1, wherein initializingthe Kalman filter comprises: initializing the Kalman filter parametersusing a preset configuration file.
 7. The method of claim 5, whereinpredicting the next center point comprises: providing a first estimatedcenter point and velocity; providing values for Kalman filterparameters; performing Kalman prediction; obtaining a predicted centerpoint and velocity; and updating prediction error variance based on thepredicted center point and velocity.
 8. The method of claim 7, whereinthe first estimated center point is the initial center point for a firstiteration.
 9. The method of claim 7, wherein checking validity of theprediction comprises: checking if the predicted center point lies withina predetermined volume; and checking if the prediction error variance isbelow an estimation threshold.
 10. The method of claim 7, wherein thestep of performing template matching comprises: providing the predictedcenter point and velocity as inputs; providing updated prediction errorvariance; performing template matching; obtaining a measured centerpoint and velocity; and obtaining measurement error variance.
 11. Themethod of claim 10, wherein the template matching is performed based onRayleigh approximation.
 12. The method of claim 10, wherein the step ofupdating the Kalman filter comprises: providing the measured centerpoint and velocity and measurement error variance as inputs; obtaining asecond estimated center point and velocity; and updating the variableKalman Gain (K).
 13. The method of claim 12, wherein the secondestimated center point is the next center point.
 14. An automatedsegmentation method for generating a surface contour of a vessel, themethod comprising: acquiring a 3-D image volume; initializing a vesselcross section; initializing an iteration counter; evolving an activecontour towards a vessel boundary; regularizing a contour from theactive contour evolution by using least square fitting; determining ifthe value of the iteration counter is greater than a predeterminedvalue; performing re initialization using the regularized contour if thevalue of the iteration counter is less than the predetermined value;incrementing the iteration counter; and repeating the steps of evolving,regularizing, determining, performing and incrementing for apredetermined number of iterations indicated by the predetermined value.15. The method of claim 14, wherein the step of initializing comprises:detecting centerline of a vessel using a vessel centerline detectionmethod; obtaining an initial center point estimated upon detecting thecenterline from the vessel centerline detection method; identifying aninitial elliptical contour; and setting up active contour parametersbased on the initial center point and the initial elliptical contour.16. The method of claim 15, wherein the step of identifying initialelliptical contour comprises steps of: determining lengths of a majorand a minor axis based on one or more gate dimensions used in the vesselcenterline detection method; and setting the orientation of the majoraxis from a reference axis approximately as equal to zero.
 17. Themethod of claim 15, wherein the step of setting up active contourparameters comprises: specifying one or more active contour parameters,the one or more active contour parameters comprising weight parametersfor area weighted mean difference, weight parameter for curve length,time step size for curve evolution, maximum re-iteration value andtolerance of contour convergence.
 18. The method of claim 14, whereinthe vessel is selected from the group consisting of blood, lymph andlacteal.
 19. A method of segmenting a vessel comprising plurality ofcross sections, the method comprising: acquiring a 3D image volume;initializing one or more Kalman filter parameters using a presetconfiguration file; selecting an initial center point within the vesselto be segmented; performing an initial segmentation on a first imageslice based on the initial center point, the first image slice being a2D cross-section of the vessel; creating a template of a vessel crosssection based on the initial segmentation; predicting a next centerpoint that is a translation of the initial point along a beam directionusing the Kalman filter; correcting the next center point based on ameasurement of the next center point in the image volume; segmenting asecond image slice based on the next center point; and repeating thesteps of predicting, correcting and segmenting until the vessel has beencompletely segmented in the 3D image volume.
 20. The method of claim 19,wherein using the Kalman filter to predict a next center pointcomprises: defining a transition matrix for translating the initialcenter point by a spatial displacement along the beam direction,defining a prediction error variance P for the initial center point,predicting a next center point and a next prediction error variancebased on the transition matrix, defining process noise variance for theprediction error variance and the series of center point measurements,correcting the center point prediction based on a measurement errorvariance, a measurement of the predicted center point, and the processnoise variance of the center point measurements, and correcting theprediction error variance based on the measurement error variance andthe process noise variance of the center point measurements.
 21. Amethod of vessel temporal tracking, the method comprising: acquiring a3D image volume; initializing one or more Kalman filter parameters usinga preset configuration file; identifying a first image slice; performingan initial segmentation on the first image slice; selecting an initialcenter point of the segmented image slice; creating a template of avessel cross section around the initial center point based on theinitial segmentation, the template being an elliptical model; finding anext center point by the steps of: copying parameters of the adaptedelliptical model to a plurality of candidate points neighboring theinitial center point; orienting an elliptical adaptable model aroundeach of the plurality of candidate points using the copied parameters;searching for center points around each of the candidate points based onthe elliptical adapted model around each of the candidate points;adapting the elliptical adaptable models around the candidate points tothe found center points; selecting one of the candidate points whoseadapted model fits best to the vessel as the next center point andrepeating the step of finding a next center point until an end point ofthe vessel centerline or a predetermined number of iterations arereached.
 22. The method of claim 21, wherein the similarity is computedusing CD₂ similarity measure.
 23. The method of claim 21, wherein thesearching step comprises performing morphological filtering to eliminatestructures smaller than a speckle size.
 24. The method of claim 23,wherein the searching step further comprises: counting the number ofcontinuous vessels in the search region; computing the area of eachcontinuous vessels; and rejecting vessels having an area lying outside apredetermined range.
 25. A computer system comprising: a processor and aprogram storage device readable by the computer system, embodying aprogram of instructions executable by the processor to perform methodsteps for automatic segmentation and temporal tracking of a blood vesselin a 2D or 3D image data set by extracting a centerline along the bloodvessel in a selected region, the method comprising: acquiring an imagevolume from an ultrasound imaging system; performing vessel segmentationon the 3D image volume to generate a 3D ultrasound image of the bloodvessel; detecting a vessel centerline for the segmented blood vessel;estimating cross-sectional area of the segmented blood vessel using thecalculated vessel centerline; and performing temporal tracking of theestimated cross-section based on template matching.
 26. The method ofclaim 25, wherein the method of detecting a vessel centerline comprisessteps of: initializing a centerline; initializing a Kalman filter;predicting a next center point using the Kalman filter; checkingvalidity of the prediction; performing template matching for estimatingmeasurement error; updating the Kalman filter based on the templatematching; and repeating the steps of predicting, checking, performingand updating for a predetermined number of times.
 27. The method ofclaim 25, wherein the method of estimating cross-sectional areacomprises steps of: initializing a vessel cross section; initializing aniteration counter; evolving an active contour towards a vessel boundary;regularizing a contour from the active contour evolution by using leastsquare fitting; determining if the value of the iteration counter isgreater than a predetermined value; performing re initialization usingthe regularized contour if the value of the iteration counter is lessthan the predetermined value; incrementing the iteration counter; andrepeating the steps of evolving, regularizing, determining, performingand incrementing for a predetermined number of iterations indicated bythe predetermined value.
 28. The computer system of claim 25, whereinthe method of performing temporal tracking comprises steps of:initializing one or more Kalman filter parameters using a presetconfiguration file; identifying a first image slice; performing aninitial segmentation on the first image slice; selecting an initialcenter point of the segmented image slice; creating a template of avessel cross section around the initial center point based on theinitial segmentation, the template being an elliptical model; finding anext center point by the steps of: copying parameters of the adaptedelliptical model to a plurality of candidate points neighboring theinitial center point; orienting an elliptical adaptable model aroundeach of the plurality of candidate points using the copied parameters;searching for center points around each of the candidate points based onthe elliptical adapted model around each of the candidate points;adapting the elliptical adaptable models around the candidate points tothe found center points; selecting one of the candidate points whoseadapted model fits best to the vessel as the next center point andrepeating the step of finding a next center point until an end point ofthe vessel centerline or a predetermined number of iterations arereached.